KDCoE [1]} is a KGE approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using descriptions of entities. With this approach, the authors have demonstrated that a very good performance can be achieved for an entity alignment task by using an Attentive Gated Recurrent Unit encoder (AGRU) to encode multilingual descriptions with multilingual word embeddings as inputs. The multilingual embedding model used is a cross-lingual Bilbowa word embedding [2]} trained on the cross-lingual parallel corpora Europarl v7 [3]} and monolingual corpora of Wikipedia dump. The results from KDCoE show that multilingual text encoders can benefit from multilingual word embeddings. It would also be interesting to adopt the same approach, which is used to encode multilingual entity descriptions for cross-lingual entity alignment task in KDCoE, for a link prediction task on different monolingual datasets such as FB15K and FB15K-237. This approach allows to capture as much information as possible from entity descriptions present in multiple languages.
